As shown in Table REF  and Figure REF , our SCL outperforms other losses, \(\) , softmax loss, center loss, and triplet loss.
It is no wonder that softmax loss performs poorly since it only focuses on finding a decision boundary to separate different classes.
As for triplet loss and center loss, though they explicitly consider intra-class compactness and inter-class separability,
the results show that indiscriminately constraining intra-class compactness of natural and manipulated faces usually leads to a sub-optimal solution.
This validates our analysis in Sec. REF  that
different face manipulation methods will produce different forgery features due to GAN fingerprints  [1]} and some unique operations,
making it nontrivial to aggregate all of the manipulated faces together.
Compared to them, our SCL adopts an asymmetric optimization goal for natural and manipulated faces to learn discriminative features,
which is more compatible with the feature distribution of samples.
